Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

Mustafa Ghaderzadeh,Azamossadat Hosseini,Farkhondeh Asadi,Arash Roshanpour,Hassan Abolghasemi,Pengwei Wang,Davood Bashash

doi:10.1155/2021/9933481

Mustafa Ghaderzadeh, Azamossadat Hosseini + Show 5 more

Open Access

https://doi.org/10.1155/2021/9933481

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Abstract

Introduction. The early detection and diagnosis of leukemia, i.e., the precise differentiation of malignant leukocytes with minimum costs in the early stages of the disease, is a major problem in the domain of disease diagnosis. Despite the high prevalence of leukemia, there is a shortage of flow cytometry equipment, and the methods available at laboratory diagnostic centers are time-consuming. Motivated by the capabilities of machine learning (machine learning (ML)) in disease diagnosis, the present systematic review was conducted to review the studies aiming to discover and classify leukemia by using machine learning. Methods. A systematic search in four databases (PubMed, Scopus, Web of Science, and ScienceDirect) and Google Scholar was performed via a search strategy using Machine Learning (ML), leukemia, peripheral blood smear (PBS) image, detection, diagnosis, and classification as the keywords. Initially, 116 articles were retrieved. After applying the inclusion and exclusion criteria, 16 articles remained as the population of the study. Results. This review study presents a comprehensive and systematic view of the status of all published ML-based leukemia detection and classification models that process PBS images. The average accuracy of the ML methods applied in PBS image analysis to detect leukemia was >97%, indicating that the use of ML could lead to extraordinary outcomes in leukemia detection from PBS images. Among all ML techniques, deep learning (DL) achieved higher precision and sensitivity in detecting different cases of leukemia, compared to its precedents. ML has many applications in analyzing different types of leukemia images, but the use of ML algorithms to detect acute lymphoblastic leukemia (ALL) has attracted the greatest attention in the fields of hematology and artificial intelligence. Conclusion. Using the ML method to process leukemia smear images can improve accuracy, reduce diagnosis time, and provide faster, cheaper, and safer diagnostic services. In addition to the current diagnostic methods, clinical and laboratory experts can also adopt ML methods in laboratory applications and tools.

Highlights

Among all types of blood cancers, leukemia is the most common form of malignancy in different age groups, especially in children. is abnormal phenomenon is caused by excessive proliferation and immature growth of blood cells, which can damage red blood cells, bone marrow, and the defense system
ALL-IDB, one of the most well-known datasets published in two versions, has been utilized in many articles, most of which have diagnosed and classified acute lymphoblastic leukemia (ALL) via different Machine Learning (ML) techniques [16,17,18,19,20,21]. ere is another published leukemia dataset called Benchmark for the development of ML algorithms, used by some studies
Blood smear image analysis is a vital role in the diagnosis of many blood-related diseases. e diagnosis of leukemia in its early stages and the first smears can lead to immediate diagnosis and the quick initiation of the treatment

Summary

Introduction

Among all types of blood cancers, leukemia is the most common form of malignancy in different age groups, especially in children. is abnormal phenomenon is caused by excessive proliferation and immature growth of blood cells, which can damage red blood cells, bone marrow, and the defense system. Among all types of blood cancers, leukemia is the most common form of malignancy in different age groups, especially in children. Ere are different types of leukemia that hematologists in cell transplant laboratories can differentiate/ diagnose based on microscopic images. An early diagnosis of leukemia has always been a challenge to researchers, doctors, and hematologists. Leukemia diagnosis is difficult in its early stages due to the mild nature of the symptoms. E most common leukemia diagnosis method is the microscopic evaluation of PBS, but the golden standard for leukemia diagnosis only involves taking and analyzing bone marrow samples [3,4,5,6] Leukemia diagnosis is difficult in its early stages due to the mild nature of the symptoms. e most common leukemia diagnosis method is the microscopic evaluation of PBS, but the golden standard for leukemia diagnosis only involves taking and analyzing bone marrow samples [3,4,5,6]

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